Divergence Control

Divergence Control

Purpose

Instances naturally diverge as they think different thoughts. This skill manages that divergence:

• Prevent wild deviation (instances completely disagreeing)
• Allow productive variance (different approaches to same problem)
• Maintain coherence (all instances solving related problems)

The Problem

Too much control: All instances think identically (no benefit) Too little control: Instances diverge so much they're solving different problems

Just right: Instances explore different solution paths while staying on the same problem.

Core Pattern

Instance 1: Path A ─┐
Instance 2: Path B ─┼─ Stay coherent
Instance 3: Path C ─┤  (same problem,
Instance 4: Path D ─┘   different approaches)

Key Features

1. Problem Anchoring - All instances address the same core question
2. Variance Measurement - How different ARE the outputs?
3. Coherence Thresholds - How different is TOO different?
4. Periodic Synchronization - "Check in, are we still on the same track?"
5. Guided Divergence - "Here's a direction we haven't explored yet"

Implementation

See: .claude/skills/divergence-control/divergence_manager.py

The Balance

• 0% divergence = Waste of resources
• 100% divergence = Incoherent output
• 30-50% divergence = Optimal exploration

Payment Anchor

DOGE: DC8HBTfn7Ym3UxB2YSsXjuLxTi8HvogwkV